



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a      :  int			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     """huggingface/label-files"""
			UpperCAmelCase_:		Optional[int]							      =     """imagenet-1k-id2label.json"""
			UpperCAmelCase_:		Union[str, Any]							      =     json.load(open(hf_hub_download(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
			UpperCAmelCase_:		Tuple							      =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase_:		int							      =     """std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCAmelCase_:		Any							      =     BitConfig(
			    conv_layer=lowerCAmelCase__		,						num_labels=1_0_0_0		,						idalabel=lowerCAmelCase__		,						labelaid=lowerCAmelCase__		,						)

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]  ):





			"""simple docstring"""


			if "stem.conv" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""stem.conv"""		,						"""bit.embedder.convolution"""  )
			if "blocks" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""blocks"""		,						"""layers"""  )
			if "head.fc" in name:
						UpperCAmelCase_:		int							      =     name.replace("""head.fc"""		,						"""classifier.1"""  )
			if name.startswith("""norm"""  ):
						UpperCAmelCase_:		List[Any]							      =     """bit.""" + name
			if "bit" not in name and "classifier" not in name:
						UpperCAmelCase_:		str							      =     """bit.encoder.""" + name

			return name







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Optional[int]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )
			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		str=False  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     get_config(lowerCAmelCase__  )

			# load original model from timm
			UpperCAmelCase_:		str							      =     create_model(lowerCAmelCase__		,						pretrained=lowerCAmelCase__  )
			timm_model.eval()

			# load state_dict of original model
			UpperCAmelCase_:		Optional[Any]							      =     timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCAmelCase_:		Any							      =     state_dict.pop(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[Any]							      =     val.squeeze() if """head""" in key else val

			# load HuggingFace model
			UpperCAmelCase_:		Union[str, Any]							      =     BitForImageClassification(lowerCAmelCase__  )
			model.eval()
			model.load_state_dict(lowerCAmelCase__  )

			# create image processor
			UpperCAmelCase_:		Any							      =     create_transform(**resolve_data_config({}		,						model=lowerCAmelCase__  )  )
			UpperCAmelCase_:		List[Any]							      =     transform.transforms

			UpperCAmelCase_:		Tuple							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			UpperCAmelCase_:		int							      =     BitImageProcessor(
			    do_resize=lowerCAmelCase__		,						size={"""shortest_edge""": timm_transforms[0].size}		,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,						do_center_crop=lowerCAmelCase__		,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,						do_normalize=lowerCAmelCase__		,						image_mean=timm_transforms[-1].mean.tolist()		,						image_std=timm_transforms[-1].std.tolist()		,						)

			UpperCAmelCase_:		Optional[Any]							      =     prepare_img()
			UpperCAmelCase_:		Tuple							      =     transform(lowerCAmelCase__  ).unsqueeze(0  )
			UpperCAmelCase_:		Dict							      =     processor(lowerCAmelCase__		,						return_tensors="""pt"""  ).pixel_values

			# verify pixel values
			assert torch.allclose(lowerCAmelCase__		,						lowerCAmelCase__  )

			# verify logits
			with torch.no_grad():
						UpperCAmelCase_:		Dict							      =     model(lowerCAmelCase__  )
						UpperCAmelCase_:		str							      =     outputs.logits

			print("""Logits:"""		,						logits[0, :3]  )
			print("""Predicted class:"""		,						model.config.idalabel[logits.argmax(-1  ).item()]  )
			UpperCAmelCase_:		List[str]							      =     timm_model(lowerCAmelCase__  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(lowerCAmelCase__		,						outputs.logits		,						atol=1e-3  )
			print("""Looks ok!"""  )

			if pytorch_dump_folder_path is not None:
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			if push_to_hub:
						print(F'Pushing model {model_name} and processor to the hub'  )
						model.push_to_hub(F'ybelkada/{model_name}'  )
						processor.push_to_hub(F'ybelkada/{model_name}'  )


if __name__ == "__main__":
	a      :  Dict			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='resnetv2_50x1_bitm',
	    type=str,
	    help='Name of the BiT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model to the hub.',
	)

	a      :  Tuple			     =			parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






